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Hybrid pathways for methane production: Merging thermodynamic insights with machine learning

Azita Etminan, Peter Holliman Orcid Logo, Peyman Karimi Orcid Logo, Majid Majd, Ian Mabbett Orcid Logo, Mary Larimi Orcid Logo, Ciaran Martin Orcid Logo, Anna RL. Carter Orcid Logo

Journal of Cleaner Production, Volume: 526, Start page: 146662

Swansea University Authors: Azita Etminan, Peter Holliman Orcid Logo, Ian Mabbett Orcid Logo, Mary Larimi Orcid Logo

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DOI (Published version): 10.1016/j.jclepro.2025.146662

Abstract

A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH4 production via CO and CO2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs...

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Published in: Journal of Cleaner Production
ISSN: 0959-6526 1879-1786
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa70382
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spelling 2025-09-18T12:16:37.2601573 v2 70382 2025-09-18 Hybrid pathways for methane production: Merging thermodynamic insights with machine learning d5a3f47a4f165a951b8500ec34b03085 Azita Etminan Azita Etminan true false c8f52394d776279c9c690dc26066ddf9 0000-0002-9911-8513 Peter Holliman Peter Holliman true false 5363e29b6a34d3e72b5d31140c9b51f0 0000-0003-2959-1716 Ian Mabbett Ian Mabbett true false db028d01b9d62d39518f147f6bb08fa5 0000-0001-5566-171X Mary Larimi Mary Larimi true false 2025-09-18 A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH4 production via CO and CO2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs free energy minimization identified optimal reaction conditions at moderate temperatures (150–250 °C) and elevated pressures, achieving over 98 % CO2 conversion with less than 1 wt% carbon formation. In parallel, machine learning models were developed using an augmented dataset of 2777 experimental observations. Atomic-level structural and electronic descriptors were incorporated into the dataset, including unit cell density and formation energy for active metals, promoters, and supports. Feature selection through Pearson correlation and RFECV identified active phase weight, support density, and reduction conditions as the most influential variables. Among all tested algorithms, XGBoost and CatBoost demonstrated the highest accuracy, with R2 values exceeding 0.93 for predicting CH4 yield, selectivity, and CO2 conversion. SHAP and partial dependence analyses showed that catalyst stability and textural properties govern overall performance. This integrated thermodynamic and machine learning approach defines the operating limits for high-efficiency methanation and provides a data-driven framework for catalyst optimization in industrial applications. Journal Article Journal of Cleaner Production 526 146662 Elsevier BV 0959-6526 1879-1786 Thermodynamic analysis; Catalyst selection; Machine learning; CO2 Methanation; Energy and Exergy Analysis 1 10 2025 2025-10-01 10.1016/j.jclepro.2025.146662 COLLEGE NANME COLLEGE CODE Swansea University SU Library paid the OA fee (TA Institutional Deal) We gratefully thank EPSRC and Tata Steel for co-sponsoring an iCASE PhD studentship (Voucher number 220106) for AE and EPSRC for funding the Sustain Hub (EP/S018107/1) for PJH and the Centre for Digital Citizens - Next Stage Digital Economy Centre (EP/T022582/1) for ARLC. 2025-09-18T12:16:37.2601573 2025-09-18T10:51:40.9838545 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering Azita Etminan 1 Peter Holliman 0000-0002-9911-8513 2 Peyman Karimi 0000-0003-2936-7458 3 Majid Majd 4 Ian Mabbett 0000-0003-2959-1716 5 Mary Larimi 0000-0001-5566-171X 6 Ciaran Martin 0009-0008-1395-0466 7 Anna RL. Carter 0000-0002-2436-666X 8 70382__35124__99ee46bcd26244748a91bdf31b19b1ca.pdf 70382.VOR.pdf 2025-09-18T12:11:22.2179361 Output 18367363 application/pdf Version of Record true © 2025 The Authors. This is an open access article distributed under the terms of the Creative Commons CC-BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
spellingShingle Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
Azita Etminan
Peter Holliman
Ian Mabbett
Mary Larimi
title_short Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
title_full Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
title_fullStr Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
title_full_unstemmed Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
title_sort Hybrid pathways for methane production: Merging thermodynamic insights with machine learning
author_id_str_mv d5a3f47a4f165a951b8500ec34b03085
c8f52394d776279c9c690dc26066ddf9
5363e29b6a34d3e72b5d31140c9b51f0
db028d01b9d62d39518f147f6bb08fa5
author_id_fullname_str_mv d5a3f47a4f165a951b8500ec34b03085_***_Azita Etminan
c8f52394d776279c9c690dc26066ddf9_***_Peter Holliman
5363e29b6a34d3e72b5d31140c9b51f0_***_Ian Mabbett
db028d01b9d62d39518f147f6bb08fa5_***_Mary Larimi
author Azita Etminan
Peter Holliman
Ian Mabbett
Mary Larimi
author2 Azita Etminan
Peter Holliman
Peyman Karimi
Majid Majd
Ian Mabbett
Mary Larimi
Ciaran Martin
Anna RL. Carter
format Journal article
container_title Journal of Cleaner Production
container_volume 526
container_start_page 146662
publishDate 2025
institution Swansea University
issn 0959-6526
1879-1786
doi_str_mv 10.1016/j.jclepro.2025.146662
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
active_str 0
description A comprehensive study was conducted to simultaneously simulate thermodynamic behavior and predict catalyst performance for CH4 production via CO and CO2 methanation, using blast furnace gas (BFG) and basic oxygen furnace gas (BOFG) as feedstocks. Thermodynamic equilibrium simulations based on Gibbs free energy minimization identified optimal reaction conditions at moderate temperatures (150–250 °C) and elevated pressures, achieving over 98 % CO2 conversion with less than 1 wt% carbon formation. In parallel, machine learning models were developed using an augmented dataset of 2777 experimental observations. Atomic-level structural and electronic descriptors were incorporated into the dataset, including unit cell density and formation energy for active metals, promoters, and supports. Feature selection through Pearson correlation and RFECV identified active phase weight, support density, and reduction conditions as the most influential variables. Among all tested algorithms, XGBoost and CatBoost demonstrated the highest accuracy, with R2 values exceeding 0.93 for predicting CH4 yield, selectivity, and CO2 conversion. SHAP and partial dependence analyses showed that catalyst stability and textural properties govern overall performance. This integrated thermodynamic and machine learning approach defines the operating limits for high-efficiency methanation and provides a data-driven framework for catalyst optimization in industrial applications.
published_date 2025-10-01T05:26:28Z
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score 11.090009

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